Updated model for identifying habitable zones around stars puts Earth on the edge

Researchers at Penn state have developed a new method for calculating the habitable zone around stars. The computer model based on new greenhouse gas databases provides a tool to better estimate which extrasolar planets with sufficient atmospheric pressure might be able to maintain liquid water on their surface. The new model indicates that some of the nearly 300 possible Earth-like planets previously identified might be too close to their stars to to be habitable.

So far, scientists have found some 18,000 extrasolar planet candidates with only a handful of these the right size, distance and having the proper orbital characteristics to be potentially habitable. “Habitable” is very broadly defined as being very approximately the right size and having a temperature where liquid water could exist on the surface of the planet. It’s a very generous definition, but it’s still one that leaves a very large margin of error.

Part of the reason is the variables the scientists use to calculate the habitable zone. One half of the equation is the star itself. Is it old? Is it young? Is it hot? Is it cool? Is it a variable? These determine how far the habitable zone is from the star and how wide it is. Then there is the planet itself, with characteristics such as size and temperature used to fine tune the estimates.

The Penn State model is based on previous work by James Kasting, Evan Pugh Professor of Geosciences also at Penn State. In the current study, the habitable zone is calculated based on stellar ﬂux incident on a planet, that is, the amount of light falling on it, instead of its equilibrium temperature.

It is not, however, a way of coming up with a simple temperature reading. Instead, it’s a complex computer model based on assumptions about the atmosphere of the planet and how it absorbs and radiates heat under given conditions. Even though these calculations are so involved they need a supercomputer to carry them out, they are still very simplified compared to reality and operate on a number of assumptions. For example, this study assumes a one-dimensional, radiative-convective, cloud-free climate. The team themselves admit that some factors may have been under or overestimated and the results will reflect this.